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from datetime import datetime

from airflow import DAG
from airflow.models import Variable
from airflow.providers.amazon.aws.operators.emr import EmrServerlessStartJobOperator

APPLICATION_ID = Variable.get("emr_serverless_application_id")
JOB_ROLE_ARN = Variable.get("emr_serverless_job_role")
S3_LOGS_BUCKET = Variable.get("emr_serverless_log_bucket")

with DAG(
    dag_id='example_emr_serverless_job',
    schedule_interval=None,
    start_date=datetime(2021, 1, 1),
    tags=['example'],
    catchup=False,
) as dag:

    # An example of how to get the cluster id and arn from an Airflow connection
    # APPLICATION_ID = '{{ conn.emr_eks.extra_dejson["virtual_cluster_id"] }}'
    # JOB_ROLE_ARN = '{{ conn.emr_eks.extra_dejson["job_role_arn"] }}'

    # [START howto_operator_emr_serverless_job]
    job_starter = EmrServerlessStartJobOperator(
        task_id="start_job",
        application_id=APPLICATION_ID,
        execution_role_arn=JOB_ROLE_ARN,
        job_driver={
            "sparkSubmit": {
                "entryPoint": "local:///usr/lib/spark/examples/src/main/python/pi.py",
            }
        },
        configuration_overrides={
            "monitoringConfiguration": {
                "s3MonitoringConfiguration": {
                    "logUri": f"s3://{S3_LOGS_BUCKET}/logs/"
                }
            },
        },
        config={"name": "sample-job"}
    )
    # [END howto_operator_emr_serverless_job]